@inproceedings{bassanini-etal-2026-quid,
    title = "Quid est {VERITAS}? A Modular Framework for Archival Document Analysis",
    author = "Bassanini, Leonardo  and
      Biancardi, Ludovico  and
      Ferrara, Alfio  and
      Gamberini, Andrea  and
      Picascia, Sergio  and
      Vaglienti, Folco",
    editor = "Montejo-Raez, Arturo  and
      Grisot, Cristina  and
      Blochowiak, Joanna  and
      Ljube{\v{s}}i{\'c}, Nikola  and
      Battaner, Elena  and
      Rigau, German",
    booktitle = "Proceedings of Shaping Multilingual, Multimodal {AI} for the Social Sciences and Humanities ({LLM}s4{SSH}) @ {LREC} 2026",
    month = may,
    year = "2026",
    address = "Palma de Mallorca (Spain)",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/revision-previews/2026.llms4ssh-1.6/",
    doi = "10.63317/3ec9hbgdgs8x",
    pages = "57--66",
    abstract = "The digitisation of historical documents has traditionally been conceived as a process limited to character-level transcription, producing flat text that lacks the structural and semantic information necessary for substantive computational analysis. We present VERITAS (Vision-Enhanced Reading, Interpretation, and Transcription of Archival Sources), a modular, model-agnostic framework that reconceptualises digitisation as an integrated workflow encompassing transcription, layout analysis, and semantic enrichment. The pipeline is organised into four stages{---}Preprocessing, Extraction, Refinement, and Enrichment{---}and employs a schema-driven architecture that allows researchers to declaratively specify their extraction objectives. We evaluate VERITAS on the critical edition of Bernardino Corio{'}s Storia di Milano, a Renaissance chronicle of over 1,600 pages. Results demonstrate that the pipeline achieves a 67.6{\%} relative reduction in word error rate compared to a commercial OCR baseline, with a threefold reduction in end-to-end processing time when accounting for manual correction. We further illustrate the downstream utility of the pipeline{'}s output by querying the transcribed corpus through a retrieval-augmented generation system, demonstrating its capacity to support historical inquiry."
}